




"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase				:     Tuple					=				"""▁"""

lowerCAmelCase				:     int					=				{"""vocab_file""": """spiece.model"""}

lowerCAmelCase				:     Any					=				{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

lowerCAmelCase				:     Tuple					=				{
    """google/pegasus-xsum""": 512,
}


lowerCAmelCase				:     List[str]					=				logging.get_logger(__name__)






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		VOCAB_FILES_NAMES

				__UpperCamelCase         =		VOCAB_FILES_NAMES
				__UpperCamelCase         =		PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase         =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase         =		["input_ids", "attention_mask"]





				def __init__(			self						,					_a						,					_a="<pad>"						,					_a="</s>"						,					_a="<unk>"						,					_a="<mask_2>"						,					_a="<mask_1>"						,					_a=None						,					_a=103						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   offset
							if additional_special_tokens is not None:
										if not isinstance(_a						,					_a					):
													raise TypeError(
													    f'additional_special_tokens should be of type {type(_a					)}, but is'
													    f' {type(_a					)}'					)

										lowerCamelCase      =   (
										    ([mask_token_sent] + additional_special_tokens)
										    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
										    else additional_special_tokens
										)
										# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
										additional_special_tokens_extended += [
										    f'<unk_{i}>' for i in range(len(_a					)						,					self.offset - 1					)
										]

										if len(set(_a					)					) != len(_a					):
													raise ValueError(
													    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
													    f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'					)
										lowerCamelCase      =   additional_special_tokens_extended
							else:
										lowerCamelCase      =   [mask_token_sent] if mask_token_sent is not None else []
										additional_special_tokens += [f'<unk_{i}>' for i in range(2						,					self.offset					)]

							lowerCamelCase      =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=_a						,					unk_token=_a						,					mask_token=_a						,					pad_token=_a						,					mask_token_sent=_a						,					offset=_a						,					additional_special_tokens=_a						,					sp_model_kwargs=self.sp_model_kwargs						,					**_a						,					)
							lowerCamelCase      =   mask_token_sent
							lowerCamelCase      =   vocab_file
							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(_a					)

							# add special tokens to encoder dict
							lowerCamelCase      =   {
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
										self.encoder.update(
										    {
										        2: self.mask_token_sent,
										        3: self.mask_token,
										    }					)

							if self.offset > 0:
										# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
										# mask_token_sent is already added to list -> so start at 1
										self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1						,					self.offset - 1					)}					)

							lowerCamelCase      =   {v: k for k, v in self.encoder.items()}





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return len(self.sp_model					) + self.offset





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   {self.convert_ids_to_tokens(_a					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab





				def __getstate__(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.__dict__.copy()
							lowerCamelCase      =   None
							return state





				def __setstate__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   d

							# for backward compatibility
							if not hasattr(self						,					"""sp_model_kwargs"""					):
										lowerCamelCase      =   {}

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return self.sp_model.encode(_a						,					out_type=_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if token in self.decoder:
										return self.decoder[token]
							elif token in self.added_tokens_decoder:
										return self.added_tokens_decoder[token]
							lowerCamelCase      =   self.sp_model.piece_to_id(_a					)
							return sp_id + self.offset





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if index in self.encoder:
										return self.encoder[index]
							elif index in self.added_tokens_encoder:
										return self.added_tokens_encoder[index]
							else:
										lowerCamelCase      =   self.sp_model.IdToPiece(index - self.offset					)
							return token





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   []
							lowerCamelCase      =   """"""
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													out_string += self.sp_model.decode(_a					) + token
													lowerCamelCase      =   []
										else:
													current_sub_tokens.append(_a					)
							out_string += self.sp_model.decode(_a					)
							return out_string.strip()





				def   _lowerCAmelCase							(			self						,					_a=False					):





							"""simple docstring"""
							return 1





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   set(self.all_special_ids					)  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None						,					_a = False					):





							"""simple docstring"""
							if already_has_special_tokens:
										return self._special_token_mask(_a					)
							elif token_ids_a is None:
										return self._special_token_mask(_a					) + [1]
							else:
										return self._special_token_mask(token_ids_a + token_ids_a					) + [1]





				def   _lowerCAmelCase							(			self						,					_a						,					_a=None					):





							"""simple docstring"""
							if token_ids_a is None:
										return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							if not os.path.isdir(_a					):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
										return
							lowerCamelCase      =   os.path.join(
							    _a						,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(_a					) and os.path.isfile(self.vocab_file					):
										copyfile(self.vocab_file						,					_a					)
							elif not os.path.isfile(self.vocab_file					):
										with open(_a						,					"""wb"""					) as fi:
													lowerCamelCase      =   self.sp_model.serialized_model_proto()
													fi.write(_a					)

							return (out_vocab_file,)









"""simple docstring"""




import random



def        a__					(							snake_case__			,					snake_case__			,					snake_case__ = False							)		->   dict:
			lowerCamelCase      =   {i: [] for i in range(snake_case__							)}

			# if probability is greater or equal than 1, then generate a complete graph
			if probability >= 1:
						return complete_graph(snake_case__							)
			# if probability is lower or equal than 0, then return a graph without edges
			if probability <= 0:
						return graph

			# for each couple of nodes, add an edge from u to v
			# if the number randomly generated is greater than probability probability
			for i in range(snake_case__							):
						for j in range(i + 1			,					snake_case__							):
									if random.random() < probability:
												graph[i].append(snake_case__							)
												if not directed:
															# if the graph is undirected, add an edge in from j to i, either
															graph[j].append(snake_case__							)
			return graph



def        a__					(							snake_case__							)		->   dict:
			return {
			    i: [j for j in range(snake_case__							) if i != j] for i in range(snake_case__							)
			}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



